Traditional implanted neural devices have performed simple or specific functions in specific regions of neural anatomy, often acquiring data from a limited number of overall sensors (e.g. less than 32 sensors). Because these devices have a clearly defined signal processing chain, customized, static solid-state devices (e.g., application-specific integrated circuits or ASICs) can be designed and optimized to carry out the necessary data processing and analysis. However, as the number of sensors in a neural signal measurement/processing system increases, it has become more challenging to timely process and store the data measured by the sensors. A system with one hundred or more sensors (e.g., two hundred or more, three hundred or more) may need to contend with a similarly increasing amount of data that may be acquired at rates that exceed the processing bandwidth of a controller (e.g., implanted and/or external processor). The system processor or controller must be able to handle large volumes of data in a high-throughput fashion in order to collect as much data as possible to facilitate the thorough characterization of a neural system in nearly real-time. For neural signal measurement/processing systems that are implanted in a patient, and especially systems that are also capable of closed-loop brain stimulation based on the measured signals, a system processor or controller of this level of complexity may present a significant power, area, and throughput challenge. Depending on sensor type, geometry, location (e.g., brain region), and neural data features of interest, measurements from particular sensors may contain redundant or statistically correlated information, and/or may not contain any pertinent data at all.
Accordingly, it is desirable for neural signal measurement/processing system controllers or processors to be able to identify and process neural signals of interest, without diverting computational resources to processing signals from faulty sensors and/or sensors implanted in a patient that are measuring neural activity from a low-activity (or inactive) brain region.